High-fidelity measurements of continuum physical fields are essential for scientific discovery and engineering design but remain challenging under sparse and constrained sensing. Conventional reconstruction methods typically rely on fixed sensor layouts, which cannot adapt to evolving physical states. We propose LASER, a unified, closed-loop framework that formulates active sensing as a Partially Observable Markov Decision Process (POMDP). At its core, LASER employs a continuum field latent world model that captures the underlying physical dynamics and provides intrinsic reward feedback. This enables a reinforcement learning policy to simulate ''what-if'' sensing scenarios within a latent imagination space. By conditioning sensor movements on predicted latent states, LASER navigates toward potentially high-information regions beyond current observations. Our experiments demonstrate that LASER consistently outperforms static and offline-optimized strategies, achieving high-fidelity reconstruction under sparsity across diverse continuum fields.

翻译：连续物理场的高保真测量对于科学发现与工程设计至关重要，但在稀疏且受限的传感条件下仍面临挑战。传统重建方法通常依赖固定传感器布局，无法适应动态演变的物理状态。我们提出LASER——一种统一化的闭环框架，将主动感知建模为部分可观测马尔可夫决策过程（POMDP）。该框架的核心在于构建连续场隐式世界模型，该模型既能捕获底层物理动力学，又可提供内在奖励反馈，使强化学习策略能够在隐式想象空间中模拟“假设性”传感场景。通过基于预测隐态驱动传感器移动，LASER能够导航至当前观测范围以外的高信息潜力区域。实验表明，在多种连续场场景中，LASER在稀疏采样条件下始终优于静态及离线优化策略，实现了高保真重建性能。